I have been writing quite a bit lately about the topic of Governance 2.0/Networked Information Governance, including an earlier post in this blog. Networked Information Governance = Information Governance + Enterprise 2.0. The idea for the name came from an excellent article published by Paul Strassman in 2001. At the time of its authoring in 2001, networked business models were continuing to grow in popularity, from the military to the most agile Fortune 2000 organizations. What it pre-dated was the radical advances in collaborative technologies would occur over the next few years to bring together “informal networks” that are so relevant in the application of governance. When it comes to bringing the informal network together with a formal approach, technologies and techniques from Enterprise 2.0 are a great fit: collaboration, search, tagging and aggregation are the keys to bridging the gap.

I wrote a more detailed post on this subject on FastForward and recently posted on presentation on slideshare.

There is huge interest from clients in enterprise search, with the focus being how to create useful applications that go beyond documents or web pages.  Increasingly, we’re seeing organizations that have invested in metadata for regulatory compliance discovering the value of this asset using search technologies and techniques.

The original web experience was intended to be click-based navigating via a number of hubs to any point in the internet, but the last five years has seen the majority of users move to a language-based approach starting with a site like Google or Yahoo.  The example I often use is the rain radar, often when setting out to a meeting in a city I’ll check to see if rain is coming.  In Melbourne I can navigate from the www.bom.gov.au website to the radar but it’s faster for me to type “Melbourne weather radar” into Google, with the added benefit that I can use the same interface when I’m in Auckland, Singapore, New York etc..

At work, users are still in the late 90’s relying on incomplete intranets and a poorly maintained web of links.  The problem is primarily access to the structured repositories and even more importantly access to the structures of those repositories (ie., the metadata.

In many cases, banks have been the early adopters of metadata repositories followed by insurers and then the very large government departments.  The main driver for these repositories has been compliance and (for banks) risk (Basel II).  These repositories are enormously rich in content, but extremely difficult to interface to the rest of the organization’s information.  Search can be the solution and I recommend the following three steps:

1. Interface to metadata repositories
In a bank, a user should be able to search for “Risk Weighted Asset” and find not only the relevant documents but also a list of the systems and databases that contain relevant data as well as appropriate controls, processes and business rules.  It isn’t difficult to build interfaces between structured metadata and the search tools.

2. Interface to master data
The next step is to build an interface that allows the user to type “Assets Walmart 2005″ and find, via the metadata, appropriate queries which can then be launched in a BI tool (eg., Business Objects or Cognos).  This is part of my view that search should be the kick-off point for all information analysis.  Again, this sounds difficult but really isn’t, you can use the metadata repository to define the dimensions of search and emulate hints (ie., “Did you mean xyz”) to help if the user is almost on target.

3. Better analysis of the quality of search
The search index increasingly becomes an asset in its own right.  Using the techniques in MIKE2.0, we can use do constant health checks on the usability and relevance of the search index itself.

In this post, I want to remind readers that information is not abstract, it is something real and follows the laws of physics.  Information theory talks about encoding information using predictable patterns, which have to be represented by some type of device.  Such a device would typically use electrical energy in some form to represent each bit so it is no surprise that conservation of energy laws apply: that is creating one piece of information must destroy another.

Why does this matter?  Information is finite and discovering one thing inevitably means that something else is either lost or in some way reduced in value.  Anyone with a physics background might think of this as an extension on the Heisenberg uncertainty principle.

From a business management perspective, it means that the enterprise customer list cannot be used in an infinite number of ways without degenerating the value of the content.  While intuitively true, I argue that it is also mathematically true, through the fact that applying information such as customer details also derives information about its application.  Deriving information about its application must reduce information (significant or otherwise) from elsewhere.  Usually, this reduction is significant – the process of finding out a customer needs a new service usually reduces the confidence in earlier analysis and limits the ability to target the same customer in other ways.

In the wake of a recent adverse court finding for a major media company in Australia, I was interviewed by The Age on the business dangers of email.  I argued that communication within and between companies should be embraced rather than feared, but proper governance inevitably meant that one-on-one emails were not the best way to manage this unstructured content.

You can read the full article at http://www.theage.com.au/news/business/teamwork-avoids-dangers-of-oneonone-emails/2007/09/09/1189276544211.html or listen the podcast at the same site.

If an organization is going to move towards a Center of Excellence model for Information Development, we are some asked: “does it makes sense for this to be done offshore?” This seems a logical question, as organizations are increasingly moving their delivery capabilities offshore, especially for large application development and systems integration projects.

Although we encourage organizations to think about Information Development as a competency analogous to application development, it isn’t just something you can give to a separate group – it is a cultural change that must go across the company. While expertise can certainly be brought in from the outside, it’s also a capability that must exist internally.

Offshore Information Development should incorporate the following principles:

1. It is the governance standards, policies and processes that enable an Information Development approach. These are the same in an offshore or onshore model.

2. An Information Development team can be a physical (i.e. a dedicated team) or a virtual (i.e. members have other significant roles). In most cases there is a combination of dedicated and shared resources.
   

3. For any sizeable offshore team, it will need to contain representation as part of the Information Development Center of Excellence.
   

4. Information Development crosses business boundaries and requires participation from senior execs to line staff. Therefore, it is not a delivery capability that can be built completely offshore.
   

5. The organizational model will evolve over time and individuals in assigned roles are typically needed to drive the transition to new organizational models.

In summary, organizations should make sure they have a strong onshore capability for Information Development, even if much of their development occurs offshore. Whatever the delivery model, the key to success is Information Governance through open and common standards, architectures and policies.

We’re often asked to compare approaches to managing structured and unstructured data and attempts to bridge the gap between the two.  Traditionally, technology practitioners who worried about unstructured data have been entirely different group to those that worried about structured data.

In fact, there are three types of data, structured, unstructured and a hybrid (records-oriented) grouping of semi-structured.  They have much in common and are all part of the enterprise information landscape.  In order to look at ways to leverage the relative strengths of the different types of data, it is important to first understand how they are used.

There are three primary applications of data within most enterprises.

The first is in support of operational processes.  In the case of structured data, these processes are usually complex from a system perspective but often quite transactional from a human perspective.  In the case of semi-structured and unstructured data, there is often less system intervention or interpretation of the data with a heavy reliance on human interpretation.

Secondly, each of the three is used for analysis.  In the case of structured, it is easy to understand how the analysis is undertaken.  With semi-structured/record data, analysis can be divided into aggregation of the structured components and a manual analysis of the free-text.  With unstructured, analysis is usually restricted to searching for like terms and manually evaluating the documents.

Finally, all three types of data are used as a reference to back-up decisions and provide an audit trail for operational processes.

MIKE2.0 recommends approaches to governance, architecture and integration which are independent of the structure of the data itself.

The majority of effort associated with all data, regardless of its form, is gaining access to it at the time when it’s needed.  In all three cases, there are processes to lookup or search the data.  SQL for structured data, lookups for semi-structured and tree-oriented folders for unstructured.  Increasing, the techniques for finding all three types are converging in one set of processes called Enterprise Search.

Ironically, despite the power of search, successful implementations are really mandating the implementation of common metadata and the use of a single enterprise metadata model.  Again, MIKE2.0 takes the information architect through these requirements in a lot of detail.

In the future, organisations can expect to keep all three forms of data (structured, semi-structured records and unstructured documents) in the same repositories.  However, there is no need to wait for this future utopia to begin leveraging all three in the same applications and managing them in a common way.

Why would an executive care?  There are two main reasons why every business and technology executive should consider the quality of data modelling to be core to their success.

The first is that information is a valuable economic asset (as argued in MIKE2.0 in the article the Economic Value of Information).  Customer data, performance data, analytical information all combine to be an asset that is often worth multiples of billions of dollars.  If a company had billions of dollars worth of gold, I’d expect business executives to want to review and understand how such a valuable asset was housed!  Given that the data model is usually the main home for the information asset, the same should also be true.  The data model cannot be delegated to junior technical staff!

Increasingly there is another reason for elevating the data model.  Legacy information is becoming an obstacle to business transformation.  As the price of storage dropped during the 1990s, new systems began also storing ancillary data about the parties involved in each transaction and substantially more context for the event.  Context could, for example, include the whole sales relationship tracking leading up to a transaction, or the staff contract changes that led to a salary change.  With the context as part of the legal record, there are operational, regulatory and strategic reasons requiring that any new or transforming business function do nothing to corrupt the existing detail.  The data model is the only tool we have to map new business requirements to old data.

Given the complexity of data modelling, it’s not surprising that executives have shied away from speaking to technologists about the detail of individual models.  A discussion on normalization principles would be enough to put most decision makers off!

In the Small Worlds Data Transformation Measure article MIKE2.0 introduces a set of simple metrics to indicate whether on average the data models of an organization or doing a good job of managing the information asset.  Using the principle that information makes most sense in the context of the enterprise, it measures the level of connectivity and the degree of separation on average across a subset or all of the data models housing the information assets.

One of the concepts we introduce in MIKE2.0 is that of Networked Information Governance – essentially Information Governance combined with Enterprise 2.0. What we found when it comes to governance is the importance of the “informal network” in solving problems – the emails, hallway discussions and phone calls that place on a daily basis or in a crisis.

When it comes to bringing the information network together with a formal approach, technologies and techniques from Enterprise 2.0 are a great fit: search, tagging and aggregation are the keys to bridging the gap.

Can this approach be extended beyond Information Governance? We believe it can. Governance techniques can generally benefit from this approach – from a corporate board discussion to managing compliance with environmental regulations. Does it mean we remove the verbal conversations? Of course not (although it might have them recorded, tagged and indexed). Is security important? Definitely. The proposed solution isn’t trivial, but the Networked Information Governance Solution Offering helps define the steps required.

I’ve been reading a book called Why Not? by Yale professors Barry Nalebuff and Ian Ayers which provides “four simple tools that can help you dream up ingenious ideas for changing how we work, shop, live, and govern.” - its a highly recommended read. I was fortunate enough to attend one of Barry’s lectures last week and he’s already given me a few new ideas.

One idea is that of a Devil’s Advocate for corporate governance. It explains the religious origins of the term and the benefits of having a person that takes a counter-point for the sake of argument. This person is a trusted adviser and has a duty to take this contrarian view, therefore their argument is not as one of dissent. In the book, explanations are provided on how this technique could be applied to Corporate Governance, where strong-arm techniques can easily over-run outside opinions.

The Devil’s Advocate an interesting role on the subject of Information Governance - possibly an architect assigned within the Information Development Organisation. Although this approach could be applied more generally to any solution, I think it makes particular sense for those related Information Governance decisions, as:

• Success requires concession and buy-in from multiple parties
• Solutions are complex so multiple viewpoints are important
• It is easy for one group to dominate, but as information flows horizontally across organizations the impact of issues can be asymmetric
• It is easy to get “stuck” when someone brings up a counterpoint due to emotion and frustration. Using a devil’s advocate alternative viewpoints are quickly put on the table (it is their duty to identify issues)

We’ve all seen strong-arm techniques or argumentative competitors ruin projects.  When attempting to re-design an organisation to take a stronger focus on managing information, the shift is bound to run into issues.A trusted and educated view that raises arguments without being seen as a dissident would certainly be productive as a way to identify ownership roles, responsibilities and possible solutions.

Various motives can drive a company to undergo a large-scale “transformation” of its systems – upgrade technology, improve business processes, change an operating model. Whatever the goal, transformation efforts typically focus on the process and infrastructure changes required to improve system functionality and expand organizational capabilities. Often overlooked, however, is another key element of effective transformation – data. Without complete, accurate and useful data, transformation efforts aren’t likely to reach their goals. A data-driven approach to transformation provides the foundation to support process and infrastructure changes and meet ongoing data requirements.

In the MIKE2.0 Methodology we provided some ideas around how organizations can take a data-driven approach to mitigate risk and provide greater information-based functionality. Some good areas to go to find out more include:

• The Solution Offering
• Guiding Principles of Data Driven IT Transformation

The Exec Summary provides a presentation that summarizes the approach. We”ve used this approach effectively and would love to hear any feedback on similar experiences or the approach.